perf: Fase 1 speed+precision (V1 V11 P1 P5)
V1 Coarse-to-fine angolare:
- Al top-level valuta solo 1 variante ogni coarse_angle_factor (default 2)
- Espande ai vicini nel full-res per preservare accuracy
- Safe anche per template allungati (factor=2 non perde match)
V11 Cache matcher in-memory (LRU, capacita 8):
- Key = md5(ROI bytes + params tecnici che influenzano il training)
- Re-match con stessi parametri: train_time = 0s (era 0.5-1.5s)
- OrderedDict LRU con _cache_get_matcher / _cache_put_matcher
P1 Fit parabolico 2D bivariato:
- In _subpixel_peak ora usa stencil 3x3 completo: f(dx,dy) = a + b*dx
+ c*dy + d*dx^2 + e*dy^2 + f*dx*dy
- Argmax analytic solve di sistema 2x2; fallback separabile se det~0
- Precisione attesa: 0.1-0.3 px (era 0.5 px separabile)
P5 Golden-section angle search:
- Sostituisce 5 sample equispaziati con convergenza log(n)
- Tol 0.1 gradi, 8 iterazioni max
- Helper _score_at_angle interno per valutare score a offset arbitrario
P2 Weighted centroid plateau:
- Peso = (score - (max-0.01))^2 per enfatizzare top del plateau
Benchmark suite 16 casi (4 immagini x full/part x fast/preciso):
prima Fase 1: totale find 27.3s
dopo Fase 1: totale find 25.1s
nessuna regressione match count, alcuni casi miglioramenti precisione.
ROADMAP.md aggiornato con checklist Fase 1.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
+16
@@ -2,6 +2,22 @@
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Lista ragionata di miglioramenti futuri. Priorità = impatto / effort, non urgenza temporale.
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## Fase 1 COMPLETATA (branch `speedFase1`)
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| ID | Voce | Status | Note |
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|---|---|---|---|
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| V1 | Coarse-to-fine angolare (step coarse al top-level) | ✅ | `coarse_angle_factor=2` default, safe anche su template allungati |
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| V11 | Cache matcher in-memory LRU (capacità 8) | ✅ | Key = hash(ROI bytes + params). Re-match stesse params = train 0s |
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| P1 | Fit parabolico 2D bivariato sul peak | ✅ | `_subpixel_peak` con coefficienti a, b, c, d, e, f dalla stencil 3×3; fallback separabile |
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| P5 | Golden-section angle search | ✅ | Sostituisce 5 sample equispaziati con log(n) convergenza a tol=0.1° |
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| P2 | Weighted centroid del plateau | ✅ | Integrato in `_subpixel_peak` con peso = (score - soglia)² |
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Benchmark suite 16 scenari (4 immagini × full/part × fast/preciso):
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- Prima Fase 1: totale find 27.3s
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- Dopo Fase 1: totale find 25.1s (~8% speedup)
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- Regressione match count: nessuna (alcuni casi +1 match grazie a subpixel migliore)
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- Match auto-referenziale: offset 0.00 px, angolo 0.000° (era -3.5 px, -2.5°)
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## Performance CPU
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| Sviluppo | Effort | Speed-up atteso | Dipendenze | Priorità |
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+118
-57
@@ -26,6 +26,7 @@ della ROI (modello non-rettangolare).
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from __future__ import annotations
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import math
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import os
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from concurrent.futures import ThreadPoolExecutor
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from dataclasses import dataclass
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@@ -33,6 +34,8 @@ from dataclasses import dataclass
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import cv2
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import numpy as np
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_GOLDEN = (math.sqrt(5.0) - 1.0) / 2.0 # ≈ 0.618
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from pm2d._jit_kernels import (
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score_by_shift as _jit_score_by_shift,
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score_bitmap as _jit_score_bitmap,
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@@ -338,9 +341,10 @@ class LineShapeMatcher:
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) -> tuple[float, float]:
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"""Posizione sub-pixel del picco.
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Se c'è un plateau di valori ~massimi (spread_radius satura il peak
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su un'area) ritorna il CENTROIDE del plateau. Altrimenti fit
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parabolico 2D ±0.5 px.
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1. Plateau saturo → centroide pesato del plateau (peso = score).
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2. Altrimenti → fit quadratico 2D bivariato sui 9 vicini
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(z = a + b·dx + c·dy + d·dx² + e·dy² + f·dx·dy), argmax risolto
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analiticamente con clamping ±0.5 px.
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"""
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H, W = acc.shape
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val = float(acc[y, x])
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@@ -350,18 +354,37 @@ class LineShapeMatcher:
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patch = acc[y0:y1, x0:x1]
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plateau = patch >= val - 0.01
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if plateau.sum() > 1:
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# Centroide pesato per (score - (max-0.01))² per enfatizzare i top
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weights = np.where(plateau, patch - (val - 0.01), 0.0).astype(np.float64)
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weights = weights * weights
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total = weights.sum()
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if total > 1e-9:
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ys_idx, xs_idx = np.indices(patch.shape)
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cx_w = (xs_idx * weights).sum() / total
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cy_w = (ys_idx * weights).sum() / total
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return float(x0 + cx_w), float(y0 + cy_w)
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ys_m, xs_m = np.where(plateau)
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return float(x0 + xs_m.mean()), float(y0 + ys_m.mean())
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# Fallback parabolico
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# Fit quadratico 2D bivariato su 3x3 intorno
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if x <= 0 or x >= W - 1 or y <= 0 or y >= H - 1:
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return float(x), float(y)
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c = acc[y, x]
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dx2 = acc[y, x + 1] - 2 * c + acc[y, x - 1]
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dy2 = acc[y + 1, x] - 2 * c + acc[y - 1, x]
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dx1 = (acc[y, x + 1] - acc[y, x - 1]) / 2.0
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dy1 = (acc[y + 1, x] - acc[y - 1, x]) / 2.0
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ox = -dx1 / dx2 if abs(dx2) > 1e-6 else 0.0
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oy = -dy1 / dy2 if abs(dy2) > 1e-6 else 0.0
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# Stencil 3x3: Z[i, j] con i,j ∈ {-1, 0, +1}
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Z = acc[y - 1:y + 2, x - 1:x + 2].astype(np.float64)
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# Coefficienti da finite differences
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b_c = (Z[1, 2] - Z[1, 0]) / 2.0
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c_c = (Z[2, 1] - Z[0, 1]) / 2.0
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d_c = (Z[1, 2] + Z[1, 0] - 2.0 * Z[1, 1]) / 2.0
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e_c = (Z[2, 1] + Z[0, 1] - 2.0 * Z[1, 1]) / 2.0
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f_c = (Z[2, 2] - Z[0, 2] - Z[2, 0] + Z[0, 0]) / 4.0
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# Max: risolve [2d f; f 2e][dx;dy] = [-b;-c]
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det = 4.0 * d_c * e_c - f_c * f_c
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if abs(det) > 1e-9:
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ox = (-2.0 * e_c * b_c + f_c * c_c) / det
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oy = (-2.0 * d_c * c_c + f_c * b_c) / det
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else:
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# Fallback separabile
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ox = -b_c / (2.0 * d_c) if abs(d_c) > 1e-6 else 0.0
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oy = -c_c / (2.0 * e_c) if abs(e_c) > 1e-6 else 0.0
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ox = float(np.clip(ox, -0.5, 0.5))
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oy = float(np.clip(oy, -0.5, 0.5))
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return x + ox, y + oy
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@@ -384,16 +407,11 @@ class LineShapeMatcher:
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l'angolo con score massimo (parabolic fit sulle 3 score centrali).
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Ritorna (angle_refined, score, cx_refined, cy_refined).
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"""
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# Se il match grezzo è già quasi perfetto, NON refinare: il parabolic
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# fit su picco saturo produce spostamenti spurious di posizione e
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# angolo (esempio: modello==scena deve dare ang=0, pos=centro ROI)
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# Se il match grezzo è già quasi perfetto, NON refinare
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if original_score is not None and original_score >= 0.99:
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return (angle_deg, original_score, cx, cy)
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if search_radius is None:
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search_radius = self.angle_step_deg / 2.0
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offsets = np.linspace(-search_radius, search_radius, 5)
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best = (angle_deg, -1.0, cx, cy)
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scores_by_off: dict[float, float] = {}
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h, w = template_gray.shape
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sw = max(16, int(round(w * scale)))
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@@ -409,10 +427,10 @@ class LineShapeMatcher:
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center = (diag / 2.0, diag / 2.0)
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H, W = spread0.shape
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# Ricerca locale posizione con margine ±2 px sulla (cx, cy)
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margin = 3
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for off in offsets:
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def _score_at_angle(off: float) -> tuple[float, float, float]:
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"""Ritorna (score, best_cx, best_cy) per angolo = angle_deg + off."""
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ang = angle_deg + off
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M = cv2.getRotationMatrix2D(center, ang, 1.0)
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gray_r = cv2.warpAffine(gray_p, M, (diag, diag),
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@@ -423,22 +441,20 @@ class LineShapeMatcher:
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mag, bins = self._gradient(gray_r)
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fx, fy, fb = self._extract_features(mag, bins, mask_r)
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if len(fx) < 8:
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scores_by_off[float(off)] = 0.0
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continue
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return (0.0, cx, cy)
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dx = (fx - center[0]).astype(np.int32)
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dy = (fy - center[1]).astype(np.int32)
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# Finestra locale ±margin attorno a (cx, cy) via slicing su bitmap
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y_lo = int(cy) - margin; y_hi = int(cy) + margin + 1
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x_lo = int(cx) - margin; x_hi = int(cx) + margin + 1
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sh = y_hi - y_lo; sw = x_hi - x_lo
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acc = np.zeros((sh, sw), dtype=np.float32)
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sh_w = y_hi - y_lo; sw_w = x_hi - x_lo
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acc = np.zeros((sh_w, sw_w), dtype=np.float32)
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for i in range(len(dx)):
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ddx = int(dx[i]); ddy = int(dy[i]); b = int(fb[i])
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bit = np.uint8(1 << b)
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sy0 = y_lo + ddy; sy1 = y_hi + ddy
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sx0 = x_lo + ddx; sx1 = x_hi + ddx
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a_y0 = max(0, -sy0); a_y1 = sh - max(0, sy1 - H)
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a_x0 = max(0, -sx0); a_x1 = sw - max(0, sx1 - W)
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a_y0 = max(0, -sy0); a_y1 = sh_w - max(0, sy1 - H)
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a_x0 = max(0, -sx0); a_x1 = sw_w - max(0, sx1 - W)
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s_y0 = max(0, sy0); s_y1 = min(H, sy1)
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s_x0 = max(0, sx0); s_x1 = min(W, sx1)
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if s_y1 > s_y0 and s_x1 > s_x0:
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@@ -448,31 +464,39 @@ class LineShapeMatcher:
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).astype(np.float32)
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acc /= len(dx)
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_, max_val, _, max_loc = cv2.minMaxLoc(acc)
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scores_by_off[float(off)] = float(max_val)
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if max_val > best[1]:
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new_cx = x_lo + float(max_loc[0])
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new_cy = y_lo + float(max_loc[1])
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best = (ang, float(max_val), new_cx, new_cy)
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return (float(max_val),
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float(x_lo + max_loc[0]), float(y_lo + max_loc[1]))
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# Parabolic fit su 3 angoli attorno al massimo
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sorted_offs = sorted(scores_by_off.keys())
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best_off = best[0] - angle_deg
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try:
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i = sorted_offs.index(
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min(sorted_offs, key=lambda x: abs(x - best_off))
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)
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if 0 < i < len(sorted_offs) - 1:
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s0 = scores_by_off[sorted_offs[i - 1]]
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s1 = scores_by_off[sorted_offs[i]]
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s2 = scores_by_off[sorted_offs[i + 1]]
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denom = (s0 - 2 * s1 + s2)
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if abs(denom) > 1e-6:
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delta = 0.5 * (s0 - s2) / denom
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step = sorted_offs[i + 1] - sorted_offs[i]
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refined_off = sorted_offs[i] + delta * step
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return (angle_deg + refined_off, best[1], best[2], best[3])
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except ValueError:
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pass
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# Golden-section search su [-search_radius, +search_radius]:
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# converge in log tempo a precisione ~0.1°, ~8 valutazioni vs 5
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# ma centrate su picco reale (non sample equispaziati).
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a_lo = -search_radius
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a_hi = +search_radius
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x1 = a_hi - _GOLDEN * (a_hi - a_lo)
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x2 = a_lo + _GOLDEN * (a_hi - a_lo)
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s1, cx1, cy1 = _score_at_angle(x1)
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s2, cx2, cy2 = _score_at_angle(x2)
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# Score all'origine come riferimento (ang offset 0)
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s0, cx0_s, cy0_s = _score_at_angle(0.0)
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best = (angle_deg, s0, cx0_s, cy0_s)
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tol = 0.1 # gradi
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for _ in range(8):
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if s1 > best[1]:
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best = (angle_deg + x1, s1, cx1, cy1)
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if s2 > best[1]:
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best = (angle_deg + x2, s2, cx2, cy2)
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if abs(a_hi - a_lo) < tol:
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break
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if s1 > s2:
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a_hi = x2
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x2 = x1; s2 = s1; cx2 = cx1; cy2 = cy1
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x1 = a_hi - _GOLDEN * (a_hi - a_lo)
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s1, cx1, cy1 = _score_at_angle(x1)
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else:
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a_lo = x1
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x1 = x2; s1 = s2; cx1 = cx2; cy1 = cy2
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x2 = a_lo + _GOLDEN * (a_hi - a_lo)
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s2, cx2, cy2 = _score_at_angle(x2)
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return best
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def _verify_ncc(
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@@ -523,6 +547,7 @@ class LineShapeMatcher:
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subpixel: bool = True,
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verify_ncc: bool = True,
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verify_threshold: float = 0.4,
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coarse_angle_factor: int = 2,
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) -> list[Match]:
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if not self.variants:
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raise RuntimeError("Matcher non addestrato: chiamare train() prima.")
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@@ -564,7 +589,30 @@ class LineShapeMatcher:
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def _rescore(score: np.ndarray, bg: np.ndarray) -> np.ndarray:
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return np.maximum(0.0, (score - bg) / (1.0 - bg + 1e-6))
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# Pruning varianti via top-level (parallelizzato)
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# Coarse-to-fine angolare:
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# 1) Raggruppa varianti per scala, ordina per angolo
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# 2) Top-level: valuta solo 1 ogni coarse_angle_factor varianti
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# 3) Espandi ai vicini nel full-res
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variants_by_scale: dict[float, list[int]] = {}
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for vi, var in enumerate(self.variants):
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variants_by_scale.setdefault(var.scale, []).append(vi)
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coarse_idx_list: list[int] = [] # varianti da valutare al top
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neighbor_map: dict[int, list[int]] = {} # vi_coarse -> indici vicini
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cf = max(1, coarse_angle_factor)
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for scale_key, vi_list in variants_by_scale.items():
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vi_sorted = sorted(vi_list, key=lambda i: self.variants[i].angle_deg)
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n = len(vi_sorted)
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for i in range(0, n, cf):
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vi_c = vi_sorted[i]
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coarse_idx_list.append(vi_c)
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# Vicini: ±cf/2 attorno a i (stessa scala)
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half = cf // 2
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start = max(0, i - half)
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end = min(n, i + half + 1)
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neighbor_map[vi_c] = vi_sorted[start:end]
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# Pruning varianti via top-level (parallelizzato) - solo coarse
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def _top_score(vi: int) -> tuple[int, float]:
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var = self.variants[vi]
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lvl = var.levels[min(top, len(var.levels) - 1)]
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@@ -574,17 +622,30 @@ class LineShapeMatcher:
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score = _rescore(score, bg_cache_top[var.scale])
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return vi, float(score.max()) if score.size else -1.0
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kept_variants: list[tuple[int, float]] = []
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if self.n_threads > 1:
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kept_coarse: list[tuple[int, float]] = []
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if self.n_threads > 1 and len(coarse_idx_list) > 1:
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with ThreadPoolExecutor(max_workers=self.n_threads) as ex:
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for vi, best in ex.map(_top_score, range(len(self.variants))):
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for vi, best in ex.map(_top_score, coarse_idx_list):
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if best >= top_thresh:
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kept_variants.append((vi, best))
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kept_coarse.append((vi, best))
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else:
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for vi in range(len(self.variants)):
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for vi in coarse_idx_list:
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vi2, best = _top_score(vi)
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if best >= top_thresh:
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kept_variants.append((vi2, best))
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kept_coarse.append((vi2, best))
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# Espandi ogni coarse promosso con i suoi vicini (stessa scala,
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# angoli intermedi non valutati al top)
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expanded: set[int] = set()
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score_by_vi: dict[int, float] = {}
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for vi_c, s_top in kept_coarse:
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for vi_n in neighbor_map.get(vi_c, [vi_c]):
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expanded.add(vi_n)
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# Usa lo score del coarse come stima per il sort successivo
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score_by_vi[vi_n] = max(score_by_vi.get(vi_n, 0.0), s_top)
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kept_variants: list[tuple[int, float]] = [
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(vi, score_by_vi[vi]) for vi in expanded
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]
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if not kept_variants:
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return []
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+79
-22
@@ -9,10 +9,12 @@ Endpoint:
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"""
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from __future__ import annotations
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import hashlib
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import os
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import tempfile
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import time
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import uuid
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from collections import OrderedDict
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from pathlib import Path
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import cv2
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@@ -61,6 +63,39 @@ CACHE_DIR.mkdir(exist_ok=True)
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# Cache in-memory (soft, ricaricata da disco se mancante)
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_IMG_CACHE: dict[str, np.ndarray] = {}
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# Cache matcher addestrati: (roi_hash, params_hash) -> LineShapeMatcher
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# LRU con capacità limitata
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_MATCHER_CACHE: OrderedDict = OrderedDict()
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_MATCHER_CACHE_SIZE = 8
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def _matcher_cache_key(roi: np.ndarray, tech: dict) -> str:
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h = hashlib.md5()
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h.update(roi.tobytes())
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# Solo parametri che influenzano il training
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relevant = ("num_features", "weak_grad", "strong_grad",
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"angle_min", "angle_max", "angle_step",
|
||||
"scale_min", "scale_max", "scale_step",
|
||||
"spread_radius", "pyramid_levels")
|
||||
for k in relevant:
|
||||
h.update(f"{k}={tech.get(k)}".encode())
|
||||
h.update(f"shape={roi.shape}".encode())
|
||||
return h.hexdigest()
|
||||
|
||||
|
||||
def _cache_get_matcher(key: str):
|
||||
m = _MATCHER_CACHE.get(key)
|
||||
if m is not None:
|
||||
_MATCHER_CACHE.move_to_end(key) # LRU touch
|
||||
return m
|
||||
|
||||
|
||||
def _cache_put_matcher(key: str, matcher) -> None:
|
||||
_MATCHER_CACHE[key] = matcher
|
||||
_MATCHER_CACHE.move_to_end(key)
|
||||
while len(_MATCHER_CACHE) > _MATCHER_CACHE_SIZE:
|
||||
_MATCHER_CACHE.popitem(last=False)
|
||||
|
||||
|
||||
def _store_image(img: np.ndarray) -> str:
|
||||
iid = uuid.uuid4().hex[:12]
|
||||
@@ -375,17 +410,33 @@ def match(p: MatchParams):
|
||||
h = max(1, min(h, model.shape[0] - y))
|
||||
roi_img = model[y:y + h, x:x + w]
|
||||
|
||||
m = LineShapeMatcher(
|
||||
num_features=p.num_features,
|
||||
weak_grad=p.weak_grad, strong_grad=p.strong_grad,
|
||||
angle_range_deg=(p.angle_min, p.angle_max),
|
||||
angle_step_deg=p.angle_step,
|
||||
scale_range=(p.scale_min, p.scale_max),
|
||||
scale_step=p.scale_step,
|
||||
spread_radius=p.spread_radius,
|
||||
pyramid_levels=p.pyramid_levels,
|
||||
)
|
||||
t0 = time.time(); n = m.train(roi_img); t_train = time.time() - t0
|
||||
tech_for_cache = {
|
||||
"num_features": p.num_features,
|
||||
"weak_grad": p.weak_grad, "strong_grad": p.strong_grad,
|
||||
"angle_min": p.angle_min, "angle_max": p.angle_max,
|
||||
"angle_step": p.angle_step,
|
||||
"scale_min": p.scale_min, "scale_max": p.scale_max,
|
||||
"scale_step": p.scale_step,
|
||||
"spread_radius": p.spread_radius,
|
||||
"pyramid_levels": p.pyramid_levels,
|
||||
}
|
||||
key = _matcher_cache_key(roi_img, tech_for_cache)
|
||||
m = _cache_get_matcher(key)
|
||||
if m is None:
|
||||
m = LineShapeMatcher(
|
||||
num_features=p.num_features,
|
||||
weak_grad=p.weak_grad, strong_grad=p.strong_grad,
|
||||
angle_range_deg=(p.angle_min, p.angle_max),
|
||||
angle_step_deg=p.angle_step,
|
||||
scale_range=(p.scale_min, p.scale_max),
|
||||
scale_step=p.scale_step,
|
||||
spread_radius=p.spread_radius,
|
||||
pyramid_levels=p.pyramid_levels,
|
||||
)
|
||||
t0 = time.time(); n = m.train(roi_img); t_train = time.time() - t0
|
||||
_cache_put_matcher(key, m)
|
||||
else:
|
||||
n = len(m.variants); t_train = 0.0
|
||||
nms = p.nms_radius if p.nms_radius > 0 else None
|
||||
t0 = time.time()
|
||||
matches = m.find(
|
||||
@@ -429,17 +480,23 @@ def match_simple(p: SimpleMatchParams):
|
||||
|
||||
tech = _simple_to_technical(p, roi_img)
|
||||
|
||||
m = LineShapeMatcher(
|
||||
num_features=tech["num_features"],
|
||||
weak_grad=tech["weak_grad"], strong_grad=tech["strong_grad"],
|
||||
angle_range_deg=(tech["angle_min"], tech["angle_max"]),
|
||||
angle_step_deg=tech["angle_step"],
|
||||
scale_range=(tech["scale_min"], tech["scale_max"]),
|
||||
scale_step=tech["scale_step"],
|
||||
spread_radius=tech["spread_radius"],
|
||||
pyramid_levels=tech["pyramid_levels"],
|
||||
)
|
||||
t0 = time.time(); n = m.train(roi_img); t_train = time.time() - t0
|
||||
key = _matcher_cache_key(roi_img, tech)
|
||||
m = _cache_get_matcher(key)
|
||||
if m is None:
|
||||
m = LineShapeMatcher(
|
||||
num_features=tech["num_features"],
|
||||
weak_grad=tech["weak_grad"], strong_grad=tech["strong_grad"],
|
||||
angle_range_deg=(tech["angle_min"], tech["angle_max"]),
|
||||
angle_step_deg=tech["angle_step"],
|
||||
scale_range=(tech["scale_min"], tech["scale_max"]),
|
||||
scale_step=tech["scale_step"],
|
||||
spread_radius=tech["spread_radius"],
|
||||
pyramid_levels=tech["pyramid_levels"],
|
||||
)
|
||||
t0 = time.time(); n = m.train(roi_img); t_train = time.time() - t0
|
||||
_cache_put_matcher(key, m)
|
||||
else:
|
||||
n = len(m.variants); t_train = 0.0
|
||||
nms = tech["nms_radius"] if tech["nms_radius"] > 0 else None
|
||||
t0 = time.time()
|
||||
matches = m.find(
|
||||
|
||||
Reference in New Issue
Block a user